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Python Real-World Projects

You're reading from   Python Real-World Projects Craft your Python portfolio with deployable applications

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781803246765
Length 478 pages
Edition 1st Edition
Languages
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Author (1):
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Steven F. Lott Steven F. Lott
Author Profile Icon Steven F. Lott
Steven F. Lott
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Table of Contents (20) Chapters Close

Preface 1. Chapter 1: Project Zero: A Template for Other Projects 2. Chapter 2: Overview of the Projects FREE CHAPTER 3. Chapter 3: Project 1.1: Data Acquisition Base Application 4. Chapter 4: Data Acquisition Features: Web APIs and Scraping 5. Chapter 5: Data Acquisition Features: SQL Database 6. Chapter 6: Project 2.1: Data Inspection Notebook 7. Chapter 7: Data Inspection Features 8. Chapter 8: Project 2.5: Schema and Metadata 9. Chapter 9: Project 3.1: Data Cleaning Base Application 10. Chapter 10: Data Cleaning Features 11. Chapter 11: Project 3.7: Interim Data Persistence 12. Chapter 12: Project 3.8: Integrated Data Acquisition Web Service 13. Chapter 13: Project 4.1: Visual Analysis Techniques 14. Chapter 14: Project 4.2: Creating Reports 15. Chapter 15: Project 5.1: Modeling Base Application 16. Chapter 16: Project 5.2: Simple Multivariate Statistics 17. Chapter 17: Next Steps 18. Other Books You Might Enjoy 19. Index

11.2 Overall approach

For reference see Chapter 9, Project 3.1: Data Cleaning Base Application, specifically Approach. This suggests that the clean module should have minimal changes from the earlier version.

A cleaning application will have several separate views of the data. There are at least four viewpoints:

  • The source data. This is the original data as managed by the upstream applications. In an enterprise context, this may be a transactional database with business records that are precious and part of day-to-day operations. The data model reflects considerations of those day-to-day operations.

  • Data acquisition interim data, usually in a text-centric format. We’ve suggested using ND JSON for this because it allows a tidy dictionary-like collection of name-value pairs, and supports quite complex Python data structures. In some cases, we may perform some summarization of this raw data to standardize scores. This data may be used to diagnose and debug problems with upstream...

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